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Volumetric-modulated arc therapy vs c-IMRT in esophageal cancer:A treatment planning comparison 被引量:37
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作者 Li Yin Hao Wu +8 位作者 Jian Gong Jian-Hao Geng Fan Jiang An-Hui Shi Rong Yu Yong-Heng Li Shu-Kui Han Bo Xu Guang-Ying Zhu 《World Journal of Gastroenterology》 SCIE CAS CSCD 2012年第37期5266-5275,共10页
AIM: To compare the volumetric-modulated arc ther- apy (VMAT) plans with conventional sliding window intensity-modulated radiotherapy (c-IMRT) plans in esophageal cancer (EC). METHODS: Twenty patients with EC ... AIM: To compare the volumetric-modulated arc ther- apy (VMAT) plans with conventional sliding window intensity-modulated radiotherapy (c-IMRT) plans in esophageal cancer (EC). METHODS: Twenty patients with EC were selected, including 5 cases located in the cervical, the upper, the middle and the lower thorax, respectively. Five plans were generated with the eclipse planning sys- tem: three using c-IMRT with 5 fields (5F), 7 fields (7F) and 9 fields (gF), and two using VMAT with a single arc (1A) and double arcs (2A). The treatment plans were designed to deliver a dose of 60 Gy to the plan-ning target volume (PTV) with the same constrains in a 2.0 Gy daily fraction, 5 d a week. Plans were normal- ized to 95% of the PTV that received 100% of the pre- scribed dose. We examined the dose-volume histogram parameters of PTV and the organs at risk (OAR) such as lungs, spinal cord and heart. Monitor units (MU) and normal tissue complication probability (NTCP) of OAR were also reported. RESULTS: Both c-IMRT and VMAT plans resulted in abundant dose coverage of PTV for EC of different Io- cations. The dose conformity to PTV was improved as the number of field in c-IMRT or rotating arc in VMAT was increased. The doses to PTV and OAR in VMAT plans were not statistically different in comparison with c-IMRT plans, with the following exceptions: in cervical and upper thoracic EC, the conformity index (CI) was higher in VMAT (1A 0.78 and 2A 0.8) than in c-IMRT (5F 0.62, 7F 0.66 and 9F 0.73) and homogeneity was slightly better in c-IMRT (7F 1.09 and 9F 1.07) than in VMAT (1A 1,1 and 2A 1.09), Lung V30 was lower in VMAT (1A 12.52 and 2A 12.29) than in c-IMRT (7F 14.35 and 9F 14.81). The humeral head doses were significantly increased in VMAT as against c-IMRT. In the middle and lower thoracic EC, CI in VMAT (1A 0.76 and 2A 0.74) was higher than in c-IMRT (5F 0.63 Gy and 7F 0.67 Gy), and homogeneity was almost similar between VMAT and c-IMRT. V20 (2A 21.49 Gy vs 7F 24.59 Gy and 9F 24.16 Gy) and V30 (2A 9.73 Gy vs 5F 12.61 Gy, 7F 11.5 Gy and 9F 11.37 Gy) of lungs in VMAT were lower than in c-IMRT, but low doses to lungs (V5 and Vl0) were increased. V30 (1A 48.12 Gy vs 5F 59.2 Gy, 7F 58.59 Gy and 9F 57.2 Gy), V40 and V50 of heart in VMAT was lower than in c-IMRT. MUs in VMAT plans were significantly reduced in comparison with c-IMRT, maximum doses to the spinal cord and mean doses of lungs were similar between the two techniques. NTCP of spinal cord was 0 for all cases. NTCP of lungs and heart in VMAT were lower than in c-IMRT. The advantage of VMAT plan was enhanced by doubling the arc. CONCLUSION: Compared with c-IMRT, VMAT, especial- ly the 2A, slightly improves the OAR dose sparing, such as lungs and heart, and reduces NTCP and MU with a better PTV coverage. 展开更多
关键词 Esophageal cancer Treatment planning Intensity modulated radiotherapy Volumetric modulat-ed arc radiotherapy Normal tissue complication prob-ability
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DOUBLE-MARKOV RISK MODEL
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作者 莫晓云 周杰明 +1 位作者 欧辉 杨向群 《Acta Mathematica Scientia》 SCIE CSCD 2013年第2期333-340,共8页
Given a new Double-Markov risk model DM = (μ, Q, v, H; Y, Z) and Double-Markov risk process U = {U(t), t 〉 0}. The ruin or survival problem is addressed. Equations which the survival probability satisfied and th... Given a new Double-Markov risk model DM = (μ, Q, v, H; Y, Z) and Double-Markov risk process U = {U(t), t 〉 0}. The ruin or survival problem is addressed. Equations which the survival probability satisfied and the formulas of calculating survival probability are obtained. Recursion formulas of calculating the survival probability and analytic expression of recursion items are obtained. The conclusions are expressed by Q matrix for a Markov chain and transition probabilities for another Markov Chain. 展开更多
关键词 Q process Markov chain Double-Markov risk characterization survival prob-ability recursion formula
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A Convection-Allowing Ensemble Forecast Based on the Breeding Growth Mode and Associated Optimization of Precipitation Forecast 被引量:6
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作者 xiang li hongrang he +2 位作者 chaohui chen ziqing miao shigang bai 《Journal of Meteorological Research》 SCIE CSCD 2017年第5期955-964,共10页
A convection-allowing ensemble forecast experiment on a squall line was conducted based on the breeding growth mode (BGM). Meanwhile, the probability matched mean (PMM) and neighborhood ensemble probability (NEP... A convection-allowing ensemble forecast experiment on a squall line was conducted based on the breeding growth mode (BGM). Meanwhile, the probability matched mean (PMM) and neighborhood ensemble probability (NEP) methods were used to optimize the associated precipitation forecast. The ensemble forecast predicted the precipita- tion tendency accurately, which was closer to the observation than in the control forecast. For heavy rainfall, the pre- cipitation center produced by the ensemble forecast was also better. The Fractions Skill Score (FSS) results indicated that the ensemble mean was skillful in light rainfall, while the PMM produced better probability distribution of pre- cipitation for heavy rainfall. Preliminary results demonstrated that convection-allowing ensemble forecast could im- prove precipitation forecast skill through providing valuable probability forecasts. It is necessary to employ new methods, such as the PMM and NEP, to generate precipitation probability forecasts. Nonetheless, the lack of spread and the overprediction of precipitation by the ensemble members are still problems that need to be solved. 展开更多
关键词 convection-allowing ensemble forecast breeding growth mode (BGM) precipitation optimization prob-ability matched mean (PMM) neighborhood ensemble probability (NEP) Fractions Skill Score (FSS)
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